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酒的质量等级评定是一件十分重要的工作。鉴于酒的质量等级为分类变量不能利用传统回归模型,于是采用Logistic回归模型进行建模。在结合一次对葡萄牙清酒全面调查所获得的实际数据的基础上,利用了有序Logistic回归构建了清酒质量等级预测模型,并利用了遗传算法(GA)、粒子群算法(PSO)、遗传-粒子群算法(GA-PSO)三种方法进行优化,得出GAPSO算法比上述其他两种算法能更有效地找出全局最优解。同时,找出了一组能获得最优质量等级的数据。 相似文献
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针对Hadoop Yarn资源调度问题,为提高集群作业执行效率,提出一种基于蚁群算法与粒子群算法的自适应Hadoop资源调度算法SRSAPH.SRSAPH中,通过Hadoop Yarn跳通信机制获取负载、内存、CPU速度等属性信息初始化信息素矩阵;同时,将粒子群算法的自我认知能力与社会认知能力引入到蚁群算法,提高算法的收敛速度;此外,根据蚁群算法全局最优解的波动趋势动态调整信息素挥发系数,提高解的精度.实验表明,采用SRSAPH进行资源调度,集群的作业执行时间缩短至少10%. 相似文献
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为高效传输业务类型多样化的空间数据,该文基于高级在轨系统(AOS)虚拟信道复用技术,建立了AOS虚拟信道(VC)混合调度模型。在混合调度模型中,对异步虚拟信道提出了基于遗传-粒子群排序的调度算法,业务优先级、调度时延紧迫度及帧剩余量紧迫度是影响虚拟信道调度先后顺序的关键约束,该算法根据约束建立了遗传-粒子群适应度函数模型,进一步使粒子群体内的粒子根据遗传算法的进化算子进行位置更新,从而找到最优的异步虚拟信道调度顺序。同时,对同步虚拟信道设计了动态加权轮询调度算法,使各同步虚拟信道按照加权因子和分配的时隙数,轮流占用物理信道。仿真结果表明,该文的虚拟信道混合调度算法兼顾了异步数据的优先性、同步数据的等时性和VIP数据的紧迫性,具有更小的平均调度时延和更少的帧剩余量,满足不同业务的传输要求。 相似文献
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为高效传输业务类型多样化的空间数据,该文基于高级在轨系统(AOS)虚拟信道复用技术,建立了AOS虚拟信道(VC)混合调度模型.在混合调度模型中,对异步虚拟信道提出了基于遗传-粒子群排序的调度算法,业务优先级、调度时延紧迫度及帧剩余量紧迫度是影响虚拟信道调度先后顺序的关键约束,该算法根据约束建立了遗传-粒子群适应度函数模型,进一步使粒子群体内的粒子根据遗传算法的进化算子进行位置更新,从而找到最优的异步虚拟信道调度顺序.同时,对同步虚拟信道设计了动态加权轮询调度算法,使各同步虚拟信道按照加权因子和分配的时隙数,轮流占用物理信道.仿真结果表明,该文的虚拟信道混合调度算法兼顾了异步数据的优先性、同步数据的等时性和VIP数据的紧迫性,具有更小的平均调度时延和更少的帧剩余量,满足不同业务的传输要求. 相似文献
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天基预警系统资源调度是一项重要而棘手的问题。对预警任务特性进行了分析,在此基础上提出一种基于关键点的任务分解方法,将其转换为可求解的组合优化问题;建立了问题的约束满足模型。针对该模型规模大、变量多的特点,设计一种具有快速求解能力的改进粒子群算法进行求解,该算法采取早熟避免机制,防止粒子群算法易产生的早熟现象。实验结果表明算法能够在给定时间内求得理想的调度方案。 相似文献
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路径分配问题是光环网络中的核心问题。根据遗传算法、粒子群优化算法和蚁群算法各自的特点,提出了一种融入粒子群算法和遗传算法的混合蚁群算法,用于对光网络的最优环路径的搜索。仿真结果表明,所提出的算法在收敛速度及寻优效果方面均优于基本的蚁群算法和遗传、粒子群的混合算法,证明了所提出算法的有效性。 相似文献
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基于粒子群算法的嵌入式云计算资源调度 总被引:2,自引:0,他引:2
随着移动互联网的发展,基于嵌入式设备的云计算服务成为研究热点。在国内,嵌入式云计算目前正处于探索研究阶段,云资源管理调度是嵌入式云计算的核心技术之一,其效率直接影响嵌入式云计算系统的性能。为了提高云计算性能,本文提出一种基于粒子群优化算法的云计算任务调度模型。粒子群算法中粒子位置代表可行的资源调度方案,以云计算任务完成时间及资源负载均衡度作为目标函数,通过粒子群优化算法,找出最优资源调度方案。在matlab实验平台进行了仿真,通过大量数据模拟实验表明,该模型可以快速找到最优调度方案,提高资源利用率,具有较好的实用性和可行性。 相似文献
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贾冀婷 《微电子学与计算机》2011,28(12):68-71
针对软件可靠性分配中不易求解全局最优解这一问题,将可靠性指标分配到每个模块中,并利用改进的粒子群优化算法来搜索模型的最优解.实验结果表明,改进的粒子群优化算法在求解软件可靠性分配问题时的效果优于遗传算法等其他智能优化算法. 相似文献
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Kamal Boudjelaba Frédéric Ros Djamel Chikouche 《Circuits, Systems, and Signal Processing》2014,33(10):3195-3222
This article studies the performance of two metaheuristics, particle swarm optimization (PSO) and genetic algorithms (GA), for FIR filter design. The two approaches aim to find a solution to a given objective function but employ different strategies and computational effort to do so. PSO is a more recent heuristic search method than GA; its dynamics exploit the collaborative behavior of biological populations. Some researchers advocate the superiority of PSO over GA and highlight its capacity to solve complex problems thanks to its ease of implementation. In this paper, different versions of PSOs and GAs including our specific GA scheme are compared for FIR filter design. PSO generally outperforms standard GAs in some performance criteria, but our adaptive genetic algorithm is shown to be better on all criteria except CPU runtime. The study also underlines the importance of introducing intelligence in metaheuristics to make them more efficient by embedding self-tuning strategies. Furthermore, it establishes the potential complementarity of the approaches when solving this optimization problem. 相似文献
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Slade W.H. Ressom H.W. Musavi M.T. Miller R.L. 《Geoscience and Remote Sensing, IEEE Transactions on》2004,42(9):1915-1923
Inversion of ocean color reflectance measurements can be cast as an optimization problem, where particular parameters of a forward model are optimized in order to make the forward-modeled spectral reflectance match the spectral reflectance of a given in situ sample. Here, a simulated ocean color dataset is used to test the capability of a recently introduced global optimization process, particle swarm optimization (PSO), in the retrieval of optical properties from ocean color. The performance of the PSO method was compared with the more common genetic algorithms (GA) in terms of model accuracy and computation time. The PSO method has been shown to outperform the GA in terms of model error. Of particular importance to ocean color remote sensing is the speed advantage that PSO affords over GA. 相似文献
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Somayyeh Seyed Abdullah Mahdi Aliyari 《AEUE-International Journal of Electronics and Communications》2008,62(7):549-556
A method of using particle swarm optimization (PSO) algorithm to design electromagnetic absorber is presented. To demonstrate effectiveness of the PSO algorithm three different design cases are optimized. To reduce the local minimum traps, a modified local search strategy is employed. Each design problem is optimized using genetic algorithm (GA) and four variants of PSO algorithms, namely global PSO (gbest), local PSO (lbest), comprehensive learning PSO (CLPSO), and modified local PSO (MLPSO). The results clearly show that the MLPSO is a robust, fast, and useful optimization tool for designing absorbers. A seven-layer absorber achieved by this method has reflection coefficient below 18.7 dB from VHF to 20 GHz. 相似文献
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In this paper, we propose an efficient power control algorithm for the downlink wireless CDMA systems. The goal of our paper
is to achieve the optimum and fair resource utilization by maximizing a weighted sum utility with the power constraint. In
fact, the objective function in the power optimization problem is always nonconcave, which makes the problem difficult to
solve. We make progress in solving this type of optimization problem using PSO (particle swarm optimization). PSO is a new
evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which
can solve discontinuous, nonconvex and nonlinear problems efficiently. It’s proved that the proposed algorithm converges to
the global optimal solutions in this paper. Numerical examples show that our algorithm can guarantee the fast convergence
and fairness within a few iterations. It also demonstrates that our algorithm can efficiently solve the nonconvex optimization
problems when we study the different utility functions in more realistic settings. 相似文献
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一种改进的粒子群和K均值混合聚类算法 总被引:13,自引:1,他引:12
该文针对K均值聚类算法存在的缺点,提出一种改进的粒子群优化(PSO)和K均值混合聚类算法。该算法在运行过程中通过引入小概率随机变异操作增强种群的多样性,提高了混合聚类算法全局搜索能力,并根据群体适应度方差来确定K均值算法操作时机,增强算法局部精确搜索能力的同时缩短了收敛时间。将此算法与K均值聚类算法、基于PSO聚类算法和基于传统的粒子群K均值聚类算法进行比较,数据实验证明,该算法有较好的全局收敛性,不仅能有效地克服其他算法易陷入局部极小值的缺点,而且全局收敛能力和收敛速度都有显著提高。 相似文献
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《Antennas and Propagation, IEEE Transactions on》2009,57(6):1655-1666
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提出一种采用粒子群优化算法进行盲信号分离的新方法,为盲信号分离领域提供一种新的研究思路与方法。该方法采用峰度作为适应度函数,利用粒子群算法对由多个源信号混合而成的信号进行盲信号分离。与自然梯度法盲信号分离相比,粒子群算法精度更高,收敛速度更快,实例仿真成功地对两个图像混合信号进行了盲分离,表明了算法的有效性和优越性。 相似文献
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Li-Hong Huang Kai-Ting Shr Ming-Hung Lin Yuan-Hao Huang 《Journal of Signal Processing Systems》2016,83(3):309-328
Parallelization of Digital Signal Processing (DSP) software is an important trend in Multiprocessor System-on-Chip (MPSoC) implementation. The performance of DSP systems composed of parallelized computations depends on the scheduling technique, which must in general allocate computation and communication resources for competing tasks, and ensure that data dependencies are satisfied. In this paper, we formulate a new type of parallel task scheduling problem called Parallel Actor Scheduling (PAS) for MPSoC mapping of DSP systems that are represented as Synchronous Dataflow (SDF) graphs. In contrast to traditional SDF-based scheduling techniques, which focus on exploiting graph level (inter-actor) parallelism, the PAS problem targets the integrated exploitation of both intra- and inter-actor parallelism for platforms in which individual actors can be parallelized across multiple processing units. We first address a special case of the PAS problem in which all of the actors in the DSP application or subsystem being optimized are parallel actors (i.e., they can be parallelized to exploit multiple cores). For this special case, we develop and experimentally evaluate a two-phase scheduling framework with three work flows that involve particle swarm optimization (PSO) — PSO with a mixed integer programming formulation, PSO with simulated annealing, and PSO with a fast heuristic based on list scheduling. Then, we extend our scheduling framework to support the general PAS problem, which considers both parallel actors and sequential actors (actors that cannot be parallelized) in an integrated manner. We demonstrate that our PAS-targeted scheduling framework provides a useful range of trade-offs between synthesis time requirements and the quality of the derived solutions. We also demonstrate the performance of our scheduling framework from two aspects: simulations on a diverse set of randomly generated SDF graphs, and implementations of an image processing application and a software defined radio benchmark on a state-of-the-art multicore DSP platform. 相似文献